{"id":21605352,"url":"https://github.com/thennen/synaptogen","last_synced_at":"2025-04-11T04:04:17.396Z","repository":{"id":232201805,"uuid":"619079250","full_name":"thennen/Synaptogen","owner":"thennen","description":"A fast generative model for stochastic memory cells","archived":false,"fork":false,"pushed_at":"2024-10-09T18:15:31.000Z","size":404,"stargazers_count":6,"open_issues_count":0,"forks_count":0,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-04-11T04:04:09.362Z","etag":null,"topics":["circuit-simulation","emerging-technology","gpu","julia","machine-learning","modeling","neuromorphic-hardware","reram","simulation","verilog-a"],"latest_commit_sha":null,"homepage":"","language":"Julia","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/thennen.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-03-26T07:46:12.000Z","updated_at":"2025-03-23T15:17:11.000Z","dependencies_parsed_at":"2024-09-05T18:18:59.150Z","dependency_job_id":"e0300276-b383-45b1-b6cf-48d95bb56829","html_url":"https://github.com/thennen/Synaptogen","commit_stats":null,"previous_names":["thennen/synaptogen"],"tags_count":2,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thennen%2FSynaptogen","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thennen%2FSynaptogen/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thennen%2FSynaptogen/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thennen%2FSynaptogen/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/thennen","download_url":"https://codeload.github.com/thennen/Synaptogen/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248339286,"owners_count":21087215,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["circuit-simulation","emerging-technology","gpu","julia","machine-learning","modeling","neuromorphic-hardware","reram","simulation","verilog-a"],"created_at":"2024-11-24T20:13:00.564Z","updated_at":"2025-04-11T04:04:17.374Z","avatar_url":"https://github.com/thennen.png","language":"Julia","readme":"\u003ch1 style=\"font-size:40px\"\u003e\n  \u003cdiv style=\"display: flex; justify-content: center; align-items: center;\"\u003e\n    \u003cimg src=\"logo.png\" alt=\"Alt text for image\" style=\"width: 180px; height: auto; margin-right: 10px;\"\u003e\u003cbr\u003e\n    Synaptogen\n  \u003c/div\u003e\n\u003c/h1\u003e\n\n[![DOI](https://zenodo.org/badge/619079250.svg)](https://zenodo.org/doi/10.5281/zenodo.10942560)\n\nThis is a fast generative model for stochastic memory cells.  It helps determine how real-world devices would perform in large-scale circuits, for example when used as resistive weights in a neuromorphic system.\n\nThe model is trained on measurement data and closely replicates\n- cross-correlations and history dependence of switching parameters\n- cycle-to-cycle and device-to-device distributions\n- multi-level resistance states\n- resistance non-linearity\n\n\nIt is currently implemented in\n- Julia for machine learning and general purpose programming ([Synaptogen.jl](Synaptogen.jl))\n- Python (NumPy) ([Synaptogen.py](Synaptogen.py))\n- Verilog-A for circuit-level simulations ([Synaptogen.va](Synaptogen.va))\n\nYou can check the respective subdirectories for instructions and examples.\n\n## Publications\n\nYou can learn more about the model in the following publications:\n\n[*A high throughput generative vector autoregression model for stochastic synapses*](https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2022.941753/full)\n\n[*Synaptogen: A cross-domain generative device model for large-scale neuromorphic circuit design*](https://doi.org/10.1109/TED.2024.3427616)\n\n## Code authors\n\n- Tyler Hennen (Synaptogen.jl \u0026 Synaptogen.py)\n- Leon Brackmann (Synaptogen.va)\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fthennen%2Fsynaptogen","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fthennen%2Fsynaptogen","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fthennen%2Fsynaptogen/lists"}